The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model
Abstract
Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium-resolution spatial satellites such as Landsat and Satellite Pour l'Observation de la Terre (SPOT), especially in pluvial regions. Although high temporal resolution sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), can provide high-frequency data, the coarse ground resolutions of these sensors make them unsuitable to quantify the vegetation growth processes at fine scales. This paper introduces a new data fusion model for blending observations of high temporal resolution sensors (e.g., MODIS) and moderate spatial resolution satellites (e.g., Landsat) to produce synthetic imagery with both high-spatial and temporal resolutions. By detecting temporal change information from MODIS daily surface reflectance images, our algorithm produced high-resolution temporal synthetic Landsat data based on a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image at the beginning time (T1). The algorithm was then tested over a 185×185 km2 area located in East China. The results showed that the algorithm can produce high-resolution temporal synthetic Landsat data that were similar to the actual observations with a high correlation coefficient (r) of 0.98 between synthetic imageries and the actual observations.
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Cites
Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2006.872081 · Cited by 1,968
An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions
Zhu, X., Chen, J., Gao, F., Chen, X., & Masek, J. G. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.05.032 · Cited by 1,317
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The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally
Ju, J., & Roy, D. P. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.08.011 · Cited by 616
Roy, D. P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., & Lindquist, E. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2008.03.009 · Cited by 513
Unmixing-based multisensor multiresolution image fusion
Zhukov, B., Oertel, D., Lanzl, F., & Reinhackel, G. (1999) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.763276 · Cited by 481
Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data
Painter, T. H., Dozier, J., Roberts, D. A., Davis, R. E., & Green, R. O. (2003) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00187-6 · Cited by 359
Algorithm for global leaf area index retrieval using satellite imagery
Deng, F., Chen, J. M., Plummer, S., Chen, M., & Pisek, J. (2006) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2006.872100 · Cited by 313
Cropland distributions from temporal unmixing of MODIS data
Lobell, D. B., & Asner, G. P. (2004) · Remote Sensing of Environment · doi:10.1016/j.rse.2004.08.002 · Cited by 311
Li, Z., & Fox, J. M. (2012) · Applied Geography · doi:10.1016/j.apgeog.2011.06.018 · Cited by 304
Cited by
Zhu, X., Cai, F., Tian, J., & Williams, T. (2018) · Remote Sensing · doi:10.3390/rs10040527 · Cited by 523
Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives
Mao, Q., Peng, J., & Wang, Y. (2021) · Remote Sensing · doi:10.3390/rs13071306 · Cited by 74
Pu, R., & Bonafoni, S. (2023) · Remote Sensing Applications: Society and Environment · doi:10.1016/j.rsase.2023.100921 · Cited by 55
Chen, Y., Yang, Y., Pan, X., Meng, X., & Hu, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3183114 · Cited by 22
Metadata
- Authors
- Niu, Z.
- Venue
- Journal of Applied Remote Sensing
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2012-03-07
- Volume
- 6
- Issue
- 1
- Pages
- 063507
- ISSN
- 1931-3195 (ISSN-L)
- OpenAlex
- W1970515153
- Cited by
- 289
- References
- 18
- FWCI
- 4.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- Niu, Z. (2012). Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. Journal of Applied Remote Sensing. https://doi.org/10.1117/1.jrs.6.063507
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